Data Quality Gates
Validation checks that block bad data from propagating downstream — at ingestion, at transformation, and at publication.
A quality issue caught at ingestion is a paged owner and a fixable bug. The same issue caught at the dashboard is an executive complaining about numbers and a week of triage.
What it solves
Moves the cost of data quality from forensic to preventive. Blocks bad records from reaching downstream agents and dashboards in the first place.
How we build it
Great Expectations, Soda, dbt tests, or Monte Carlo for the actual checks. Schema and null-rate gates at ingestion; business-rule gates at transformation; freshness and row-count gates at publication. Failures page the source owner with the failed records attached.
- Schema and null-rate gates at ingestion
- Business-rule gates at transformation
- Freshness and volume gates at publication
- Owner-routed page on failure
What changes when it is in place
The data team stops being on the hook for downstream complaints about upstream issues. Bad data is caught early, attributed, and fixed by the right team.